Degree | Type | Year | Semester |
---|---|---|---|
2503740 Computational Mathematics and Data Analytics | OB | 2 | 2 |
Pre-taught knowledge will be used in the subjects of Linear Algebra, Calculation in a Variable, Computation in Several Variables, Initiation in Programming, Numerical Calculation, and Algorithmism and Combining in Graphs.
Learn to model decision-making problems in terms of linear and non-linear programs. Understand the mechanism of the simplex method. Solve linear programs, by hand and with addient software. Program non-linear programming algorithms, and use existing libraries. Introduce yourself in the field of combinatorial optimization, through selected examples.
1- Nonlinear Programming: Theory of extremes. Optimization without restrictions. Optimization with restrictions.
2- Linear Programming: Modeling in terms of linear programs. The simplex algorithm. Full Linear Programming. Linear flows over networks.
3- Combinatorial optimization: classical problems. Heuristic methods Computational complexity.
The efficient learning of the optimization must combine three activities: The study of the mathematical theory, the modeling of real problems, and the effective resolution of academic and real problems. All within the eminently practical character of the degree. The real optimization problems are very complex. When we talk about "real problems" here, we refer to simplifications of real situations that can be attacked within a reasonable time in the development of the course, which at the same time give a good image of the transversality of the fields of application of the optimization
The study of the theory will be done through recommended readings and master class lessons. It will tend to apply the methodology of the reversed classroom: Students must work the subject on their own and prepare the classes through recommended previous readings; In class the remarkable aspects are discussed, the issues raised by the students are resolved and additional aspects of interest are incorporated.
It will be practiced with specific modeling software, where possible, and with function libraries in a general programming language (C / C ++ or Python) appropriate to the student's previous training. Free and / or free software will always be used. The student will also program complete basic algorithms and solve specific problems with them.
In all aspects of teaching / learning activities, the best efforts will be made by teachers and students to avoid language and situations that can be interpreted as sexist. In order to achieve continuous improvement in this topic, everyone should collaborate to show the deviations that you observe regarding this objective.
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Classroom lectures (theoretical and practical) | 56 | 2.24 | |
Type: Autonomous | |||
Problem solving by means of programming | 60 | 2.4 | |
Theoretical problem solving | 28 | 1.12 |
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Assignments Combinatorial Optimisation | Ten percent | 0 | 0 | 2, 5, 3, 4, 16, 6, 1, 8, 14, 13, 11, 12 |
Assignments Linear Programming | Ten percent | 0 | 0 | 2, 5, 3, 4, 16, 6, 1, 8, 14, 13, 11, 12 |
Assignments NonLinear Programming | Ten percent | 0 | 0 | 2, 5, 3, 4, 16, 6, 1, 8, 14, 13, 11, 12 |
Exam Combinatorial Optimisation | Fifteen percent | 2 | 0.08 | 3, 16, 10, 6, 1, 15, 14, 11, 17, 7, 9 |
Exam Linear Programming | Twenty five percent | 2 | 0.08 | 3, 16, 10, 6, 1, 15, 14, 11, 17, 7, 9 |
Exam NonLinear Programming | Twenty five percent | 2 | 0.08 | 3, 16, 10, 6, 1, 15, 14, 11, 17, 7, 9 |
During the course the essential material will be provided to follow it. Bibliographical references and other resources will be suggested at the opportune moment of the course.